Advanced Topics in IC Design

Vortragende/r (Mitwirkende/r)
  • Yiyu Shi
  • Xiaowei Xu
Umfang3 SWS
SemesterWintersemester 2018/19
Stellung in StudienplänenSiehe TUMonline


Teilnahmekriterien & Anmeldung


The primary goal of this course is for you to acquire fundamental knowledge of artificial intelligence and experience designing a complex neural network on a resource-limited embedded platform and all of the various tradeoffs/considerations that go along with such design work. The secondary goals of the course are to focus and hone your writing and oral communication skills. By the end of the course, you should be able to assist as a system designer well equipped with knowledge of artificial intelligence and embedded system programming. • Describe common artificial intelligence algorithms for various applications, and their implementations. • Describe the basic steps involved in developing a prototype of common artificial intelligence algorithms • Develop neural networks with both the software platform and embedded hardware board provided. • Analyze and compare design alternatives such as different compression techniques regarding to economics, timing performance, size, power, etc. • Deliver quality technical reports and oral presentations. • Work effectively in a team and know how to use effective meeting management skills.


Artificial Intelligence for Embedded Systems Course Description: This course covers the fundamental of artificial intelligence including but not limited to various neural networks and their applications, the associated platforms for training, as well as the compression techniques for them to be deployed on embedded devices for mobile or edge applications. The course will include comprehensive team-based hand-on design experience of a neural network on an embedded platform for a real application of choice. Projects involve neural network design, training, compression, prototype implementation, and documentation. Group project management skills, including scheduling and project tracking are stressed. Instructor: Prof. Yiyu Shi, Department of Computer Science and Engineering, University of Notre Dame, IN, USA, ( Project Assistant: Dr. Xiaowei Xu (

Lehr- und Lernmethoden

1. Lecture • Not all content discussed in class will be on-line so taking notes during class is highly encouraged. In short, anything that is written down on the board, you can write down as well. • Attendance at lectures is required. If you must miss a lecture, please contact the instructor in advance. • Lectures will be driven by student interaction in addition to the standard lecture material. 2. Design project • The design project will be a semester long project • The design project team will consist of 4 students as approved by the instructor. Teaming is strictly required. • The design project will have several deliverables as noted with the grades. 3. Homework / Labs • There are no graded homework or labs. Optional enrichment activities will be provided but are not required. 4. Teamwork • The teams will be evaluated twice during the semester. • Teamwork does not mean that one person does the work one week and the other person does the work the next week. If you sign your name to the report, it means that you have participated in solving the problems.

Studien-, Prüfungsleistung

There is no mid-term nor is there a final exam for the class. You have to develop your own project. If you want to use anything you find online, please consult the instructor first. There are no late submissions. Any extensions must require extenuating circumstances or a priori negotiation. In short, be pro-active, not reactive. Grading: Class Attendance 5% Class Presentation 1 10% Oct. 29 – 30 Class Presentation 2 10% Dec. 3-4 Project Proposal 10% Nov. 2 Status Report 1 10% Nov. 19 Status Report 2 10% Dec. 17 Final Presentation 20% Jan. 17 Final Report 25% Jan. 31

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